Determination of equine deep digital flexor muscle volume based on distances between anatomical landmarks
•A formula was determined enabling estimating equine DDF muscle volume.•Line best fitting observed points: Ln(volume[ml]) = −1.89 + 0.98 × Ln(value C[cm3]).•The estimated volume can be useful to apply human Botox® treatment-protocols. In equine medicine the use of Botox® is experimental. Dosages are...
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Published in | Research in veterinary science Vol. 97; no. 2; pp. 397 - 399 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Elsevier Ltd
01.10.2014
Elsevier Limited |
Subjects | |
Online Access | Get full text |
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Summary: | •A formula was determined enabling estimating equine DDF muscle volume.•Line best fitting observed points: Ln(volume[ml]) = −1.89 + 0.98 × Ln(value C[cm3]).•The estimated volume can be useful to apply human Botox® treatment-protocols.
In equine medicine the use of Botox® is experimental. Dosages are determined from human treatment-protocols and limited numbers of equine studies. Determination of target-muscle volume can be helpful to extrapolate human dosages. The aim of the study was to calculate a formula enabling the estimation of the deep digital flexor muscle (DDFM) volume based on distances between anatomical landmarks.
Nineteen cadaveric limbs were collected and distance A (top of olecranon to Os carpi accessorium) and B (circumference of limb) were measured. Converting mathematical formulas, C was calculated: π × (((0.5B)/π)2) × A. DDFM volume was determined by water displacement. Linear Regression Analysis was used to analyse data.
The line best fitting the observed points was: Ln(volume[ml]) = −1.89 + 0.98 × Ln(value C[cm3]). Correlation was highest when natural logarithm was applied to both variables and was 0.97.
The calculated formula enables estimating DDFM volume of a living horse. This estimated volume can be useful to apply human Botox® treatment-protocols. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0034-5288 1532-2661 |
DOI: | 10.1016/j.rvsc.2014.08.006 |